The proposed solution was focused on aligned image pairs (Track 1). The mapping between RAW and sRGB is divided into two parts: pixel-independent mapping (e.g.  white balance, color space transformation) and local-dependent mapping (e.g.  local engancement).
The proposed method implemented the pixel-level mapping as “backend" using global colour mapping module proposed in [1]} to learn the pixel-independent mapping between RAW and sRGB. Then, the local enhancement module (encoder-decoder [2]} and ResNet structure [3]}) is added to deal with the local-dependent mapping.
Ultimately, the outputs of these three modules were fused by a set of learned weights from the weight predictor inspired by [4]}.
The method is implemented in PyTorch and trained using 8\(\times \)  RTX 3090 GPU, and batch size 2; it processes a patch in 40ms on GPU.
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